[126] Servati, Michel, “Symbolism in Boole. Its Inability to be Interpreted”, in Logic in Question, ed. by Jean Béziau et al., Birkhäuser/Springer: Cham 2022 (Studies in Universal Logic), 79–96. - Peckhaus, Volker (Mathematical Reviews, MR4719646)

Floyd, Juliet, “Truth in Russell, Early Wittgenstein and Gödel”, in: Fritz Stadler (ed.), Wittgenstein and the Vienna Circle, Springer: Cham 2023 (Vienna Circle Yearbook, 28), 179–208. - Peckhaus, Volker (Mathematical Reviews, MR4572656)

Volkert, Klaus, „Drei mathematische Freunde über Poincaré“, Philosophia Scientiæ 27.3 (2023), 129–159. - Peckhaus, Volker (zbMATH Open, ZBl. 07828598)

Adaptive Data‐Driven Models in Port‐Hamiltonian Form for Control Design - Junker, Annika; Timmermann, Julia; Trächtler, Ansgar (PAMM)
Abstract Control engineering applications usually require a model that accurately represents the dynamics of the system. In addition to classical physical modeling, powerful data‐driven approaches are gaining popularity. However, the resulting models may not be ideal for control design due to their black‐box structure, which inherently limits interpretability. Formulating the system dynamics in port‐Hamiltonian form is highly beneficial, as its valuable property of passivity enables the straightforward design of globally stable controllers while ensuring physical interpretability. In a recently published article, we presented a method for data‐driven inference of port‐Hamiltonian models for complex mechatronic systems, requiring only fundamental physical prior knowledge. The resulting models accurately represent the nonlinear dynamics of the considered systems and are physically interpretable. In this contribution, we advance our previous work by including two key elements. Firstly, we demonstrate the application of the above described data‐driven PCHD models for controller design. Preserving the port‐Hamiltonian form in the closed loop not only guarantees global stability and robustness but also ensures desired speed and damping characteristics. Since control systems based on output measurements, which are continuously measured during operation due to the feedback structure, we secondly aim to use this data. Thus, we augment the existing modeling strategy with an intelligent adaptation approach to address uncertainties and (un)predictable system changes in mechatronic systems throughout their lifecycle, such as the installation of new components, wear, or temperature fluctuations during operation. Our proposed algorithm for recursively calculated data‐driven port‐Hamiltonian models utilizes a least‐squares approach with extensions such as automatically adjusting the forgetting factor and controlling the covariance matrix trace. We demonstrate the results through model‐based application on an academic example and experimental validation on a test bench.

Online Learning With Joint State and Model Estimation - Götte, Ricarda-Samantha; Timmermann, Julia (PAMM)
Model‐based state observers require high‐quality models to deliver accurate state estimates. However, due to time or cost shortage, modeling simplifications or numerical issues, models often have severe inaccuracies that may lead to insufficient and deficient control. Instead of attempting to iteratively model these deviations, we address the challenge by the concept of joint estimation. Thus, we assume a linear combination of suitable functions to approximate the inaccuracies. The parameters of the linear combination are supposed to be time invariant and augment the model's state. Subsequently, the parameters can be identified simultaneously to the states within the observer. Referring to the principle of Occam's razor, the parameters are claimed to be sparse. Our former work shows that estimating states and model inaccuracies simultaneously by a sparsity promoting unscented Kalman filter yields not only high accuracy but also provides interpretable representations of underlying inaccuracies. Based on this work, our contribution is twofold: First, we apply our approach finally on a real‐world test bench, namely a golf robot. Within the experimental setting, we investigate closed loop behavior as well as how suitable functions need to be chosen to approximate the inaccuracies in a physically interpretable way. Results do not only provide high state estimation accuracy but also meaningful insights into the system's inaccuracies. Second, we discuss and establish a method to automatically adapt and update the model based on collected data of the linear combination during operation. Examining past parameter estimates by principal component analysis, a moving window is utilized to extract the most dominant functions. These are kept characterizing the model inaccuracies, while nondominant functions are automatically neglected and refilled with novel function candidates. After analysis and rebuilding, this updated function set is subsequently fed back into the joint estimation loop and deployed for further estimation. Hence, we give a holistic paradigm of how to analyze and combat model inaccuracies while ensuring high state estimation accuracy. Within this setting, we once more investigate closed loop behavior and yield promising results. In conclusion, we show that the proposed observer provides a helpful tool to guarantee high estimation accuracy for models with severe inaccuracies or for situations with occurring deviations during operation, for example, due to mechanical wear or temperature changes.

Fotografie: „Aristoteles am Apothekenerker des Rathauses in Lemgo“, in: Volker Peckhaus und Sebastian Luft: Philosophische Übergänge. Die Abschiedsvorlesung von Volker Peckhaus und die Antrittsvorlesung von Sebastian Luft im Oktober 2023, hg. v. Vanessa Albus, Universität Paderborn: Paderborn (PUR #163), 39 - Peckhaus, Volker

Flexible operation and integration of high-temperature heat pumps using large temperature glides - Knorr, Lukas; Schlosser, Florian; Horstmann, Nils; Divkovic, Denis; Meschede, Henning (Applied Energy)

Fotografie: „Ratio an der Lateinschule in Alfeld“, in: Volker Peckhaus und Sebastian Luft: Philosophische Übergänge. Die Abschiedsvorlesung von Volker Peckhaus und die Antrittsvorlesung von Sebastian Luft im Oktober 2023, hg. v. Vanessa Albus, Universität Paderborn: Paderborn (PUR #163), 34–35 - Peckhaus, Volker

Zermelo - Peckhaus, Volker (Neue Deutsche Biographie, Bd. 28: Wettstein - Zwoch)

Schmidt, Gunther, Mathematik als Wissenschaft in der Gesellschaft. Historische Äußerungen und aktuelle Anregungen, Springer Spektrum: Berlin 2023, xiii, 263 S. - Peckhaus, Volker (zbMATH Open, Zbl. 07782971)

Exploring digital security and privacy in relative poverty in Germany through qualitative interviews - Kostan, Anastassija; Olschar, Sara; Simko, Lucy; Acar, Yasemin (33rd USENIX Security Symposium, USENIX Security 2024, Philadelphia, PA, USA, August 14-16, 2024)

Praktiken der Förderung im inklusiven Mathematikunterricht - Häsel-Weide, Uta; Nührenbörger, M. (Beiträge zum Mathematikuntericht 2024. 57. Jahrestagung der Gesellschaft für Didaktik der Mathematik)

Voices in Dialogue: Taking Polyphony in Academic Writing Seriously - Karsten, Andrea (Written Communication)
In the past decades, the notion of voice in the theorizing and teaching of academic writing has been the subject of much debate and conceptual change, especially concerning its relation to writer identity. Many newer accounts of voice and identity in academic writing draw on the dialogical concept of voice by Bakhtin. However, some theoretical and methodological inconsistencies have surfaced in the adaptions of the concept. Working from a refinement of the dialogical notion of voice based on the concepts of polyphony and interiorization, this article presents a methodological approach for analyzing voice(s) in writing. The article presents material around the evolution of an early-career researcher’s dissertation synopsis. The material is multilayered, including the writer’s text, transcripts from an interdisciplinary peer-feedback conversation with two colleagues, and a video-stimulated interview with the writer. Excerpts of the material were analyzed to trace the polyphony of interiorized voices that influenced the writing. This focus revealed the multivoicedness of academic texts as an effect of their history of coming into being. This article contributes to the question of voice and identity in academic writing from a dialogical psycholinguistic perspective by presenting a de-reifying notion of voice grounded in an understanding of writing as a polyphonic activity, which also feeds into the formation of a writer’s self.
Keywords: Literature and Literary Theory; Communication

Forschungsverbund Kinder- und Jugendsport NRW – Transferformate für den Dialog zwischen Wissenschaft und Gesellschaft - Dahl, Stefanie; Aschebrock, Kathrin (Wissenstransfer in der Sportpädagogik)

Routing Schemes for Hybrid Communication Networks - Coy, Sam; Czumaj, Artur; Scheideler, Christian; Schneider, Philipp; Werthmann, Julian (Theoretical Computer Science)

Informatische Literalität und Medienbildung im Handeln von Lehrkräften - Herzig, Bardo; Losch, Daniel (Fragmentierung in der Lehrkräftebildung - Das Lehramtsstudium im Spannungsfeld von Professionsorientierung, Bildungstheorie und (Fach-)Wissenschaft)

Agile Gestaltung digitalisierungsbezogener Schulentwicklung in der Lehrkräftebildung - Schulze, Johanna; Herzig, Bardo; Lehberger, Regine (Lehrkräftebildung in der digitalen Welt. Zukunftsorientierte Forschungs- und Praxisperspektiven)

ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification - Qudus, Umair; Röder, Michael; Vollmers, Daniel; Ngonga Ngomo, Axel-Cyrille (Proceedings of the 33rd ACM International Conference on Information and Knowledge Management)
Detecting the veracity of a statement automatically is a challenge the world is grappling with due to the vast amount of data spread across the web. Verifying a given claim typically entails validating it within the framework of supporting evidence like a retrieved piece of text. Classifying the stance of the text with respect to the claim is called stance classification. Despite advancements in automated fact-checking, most systems still rely on a substantial quantity of labeled training data, which can be costly. In this work, we avoid the costly training or fine-tuning of models by reusing pre-trained large language models together with few-shot in-context learning. Since we do not train any model, our approach ExPrompt is lightweight, demands fewer resources than other stance classification methods and can serve as a modern baseline for future developments. At the same time, our evaluation shows that our approach is able to outperform former state-of-the-art stance classification approaches regarding accuracy by at least 2 percent. Our scripts and data used in this paper are available at https://github.com/dice-group/ExPrompt.
Keywords: Stance Classification; Few-shot in-context learning; Pre-trained large language models

FaVEL: Fact Validation Ensemble Learning - Qudus, Umair; Röder, Michael; Tatkeu Pekarou, Franck Lionel; Morim da Silva, Ana Alexandra; Ngonga Ngomo, Axel-Cyrille (EKAW 2024)
Validating assertions before adding them to a knowledge graph is an essential part of its creation and maintenance. Due to the sheer size of knowledge graphs, automatic fact-checking approaches have been developed. These approaches rely on reference knowledge to decide whether a given assertion is correct. Recent hybrid approaches achieve good results by including several knowledge sources. However, it is often impractical to provide a sheer quantity of textual knowledge or generate embedding models to leverage these hybrid approaches. We present FaVEL, an approach that uses algorithm selection and ensemble learning to amalgamate several existing fact-checking approaches that rely solely on a reference knowledge graph and, hence, use fewer resources than current hybrid approaches. For our evaluation, we create updated versions of two existing datasets and a new dataset dubbed FaVEL-DS. Our evaluation compares our approach to 15 fact-checking approaches—including the state-of-the-art approach HybridFC—on 3 datasets. Our results demonstrate that FaVEL outperforms all other approaches significantly by at least 0.04 in terms of the area under the ROC curve. Our source code, datasets, and evaluation results are open-source and can be found at https://github.com/dice-group/favel.
Keywords: fact checking; ensemble learning; transfer learning; knowledge management.

KI-bezogene Kompetenzen von Lehrkräften - Herzig, Bardo (Plan BD. Online-Magazin für Schule in der Kultur der Digitalität)